Applied AI, Data & Custom Software
For the process no product on the market models, and the questions no dashboard answers. Built on your data, for your floor, by the people who mapped it.
When does a manufacturer need custom software rather than a platform?
When the process being digitized is genuinely specific to the business — a routing, a planning constraint, a customer-mandated workflow — and configuring a general platform around it produces something people work around rather than with. Custom is the right answer less often than software firms claim and more often than platform vendors admit.
Data first. Everything else depends on it.
Most failed manufacturing analytics projects did not fail at the model. They failed because the data underneath was inconsistent, incomplete across shifts, or defined differently by each system that produced it. We build the platform before we build anything on top of it.
Unified data platform
Machine telemetry, operator entries and enterprise records in one model with one definition of a part, a line, a shift and a batch.
Contextualization
Raw signals joined to what was running, who was on shift, which material lot and which order — without which no model has anything to learn from.
Quality and lineage
Completeness checks, gap detection and traceability from any number back to the sensor or the person that produced it.
Where models earn their place — and where they don't.
AI is the most oversold category in manufacturing software right now. Our position is narrow and specific: a model is worth building when there is a decision a person makes repeatedly, enough labelled history to learn from, and a clear action when the model is right.
Predictive maintenance
Condition data against failure history, on assets that fail often enough to have taught you something. Most valuable on rotating equipment with vibration or thermal signatures, and on assets where an unplanned stop cascades down the line.
Quality prediction
Process parameters against inspection outcomes, to flag a drifting batch before it reaches final inspection. Works where you have parametric data and a real defect history; does not work where quality is judged by eye and never recorded.
Anomaly and drift detection
Catching the condition nobody wrote a rule for, on processes stable enough that "normal" is definable. Often the highest-value first model, because it needs no failure labels.
Natural-language access to plant data
Letting a supervisor ask a question in words instead of learning a reporting tool. Genuinely useful, and genuinely hard to make trustworthy — which is why we ground it in your data model and show its working.
The models we will talk you out of
Demand forecasting on eighteen months of history. Predictive maintenance on an asset that has failed twice. Computer vision for a defect your inspectors catch reliably and cheaply. Any model whose output has no owner and no defined action. These get proposed constantly, they demo beautifully, and they quietly stop being used within a quarter of go-live.
Built for the process that is actually yours.
Where a platform would need so much configuration that it becomes bespoke anyway — only harder to change and dependent on someone else's roadmap.
- Operator and supervisor interfaces — designed for gloves, glare and a two-minute window
- Planning and scheduling tools — modelling your real constraints, not a generic finite-capacity engine
- Customer and supplier portals — where a contract obliges you to expose data
- Bespoke operational applications — for the process no vendor has met before
- Reporting and control-tower views — built around your review meetings, not a template
- Integration services — the connective tissue that holds a mixed estate together
Common questions.
Both models exist, and we are explicit about which applies before anything is built. Bespoke software commissioned as development work is yours, source included. Where a solution is delivered on our own platform components, those carry an ongoing license like any product would. What we will not do is blur the two, or let you discover the commercial model at renewal rather than at proposal.
It depends far more on what you are predicting than on volume. Anomaly detection can be useful within weeks because it only needs to learn normal. Failure prediction needs enough examples of the failure, which on a reliable asset can mean years. We will tell you which category you are in before anyone budgets for a model.
Yes, and we prefer to. If you have already standardized on a cloud platform or warehouse, building alongside it is lower risk and cheaper to run than introducing a parallel stack. We design to fit the estate you have.
A fair question to ask any partner. We hand over source, documentation and architecture as the work proceeds rather than at the end, and we build on mainstream technology your own team or another firm can pick up. Continuity should not depend on us being available.
Start with the audit, not the software.
A fixed-scope engagement. We walk your floor, map where data breaks down, and hand you a costed roadmap — whether or not you build any of it with us.